---
pretty_name: PhilEO-Bench Road Density Regression
task_categories:
  - other
annotations_creators:
  - other
source_datasets:
  - generated
tags:
  - image
  - geospatial
  - remote-sensing
  - earth-observation
  - satellite-imagery
  - phi-sat-2
  - simulation
  - regression
  - road-density
---

# Simulated PhiSat Bench Dataset - Roads 

This dataset comprises simulated PhiSat2 data derived from Sentinel-2, tailored for pixel-wise regression tasks aimed at estimating road coverage. 

## Dataset Overview

Each sample in the dataset includes a single-channel label. 
The labels are stored as floating-point values that represent the estimated percentage of roads area within each pixel. 
For a pixel with a 10-meter resolution (representing 100 square meters), the label values range from 0 to 100, where:

- *0* indicates no roads coverage, and
- *100* indicates full roads coverage.

# Use Case
This dataset is intended for training machine learning models for road detection, road network extraction and mapping, 
urban planning and infrastructure development, as well as disaster response and accessibility analysis.

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## Dataset Visual Summary

The figures below are designed for quick visual inspection rather than quantitative evaluation. Each representative panel shows the source image, a label overlay, and an adaptive heatmap that makes sparse targets easier to inspect.

- Samples: `378`
- Image shape: `(1, 5119, 5119)`
- Label shape: `(1, 5119, 5119)`
- Approximate mean label coverage: `0.012%`
- Approximate mean positive-pixel fraction: `0.0382`

![Representative samples](./figures/roads_preview_grid.png)

![Label distribution summary](./figures/roads_label_summary.png)
<!-- phi2fm-dataset-figures:end -->